flowchart LR
    classDef largeText font-size:18px, padding:15px;

    ML("Aprendizaje Estadístico (Y = f(X))") -->|'Y' no existe| U(Aprendizaje\nNo Supervisado)
    ML -->|'Y' existe| S(Aprendizaje\nSupervisado)

    class ML,U,S largeText;

    style ML fill:#FFFFFF, stroke:#000, width:340px, stroke-width:2px, color:#000, text-align:center;
    style U fill:#28192F66, stroke:#000, width:160px, stroke-width:2px, color:#000, text-align:center;
    style S fill:#3B2F5E66, stroke:#000, width:160px, stroke-width:2px, color:#000, text-align:center;

flowchart LR
    classDef largeText font-size:18px, padding:15px;

    ML("Aprendizaje Estadístico (Y = f(X))") -->|'Y' no existe| U(Aprendizaje\nNo Supervisado)
    ML -->|'Y' existe| S(Aprendizaje\nSupervisado)
    U -->|Se desea\ngenerar 'Y'| Clus(Agrupamiento)
    U --> |Se desea\nexplorar 'X'| DR(Reducción de\nDimensionalidad)
    S -->|'Y' es continua| Reg(Regresión)
    S --> |'Y' es categórica| Class(Clasificación)

    class ML,U,S largeText;

    style ML fill:#FFFFFF, stroke:#000, stroke-width:2px, color:#000, width:340px
    style U fill:#28192F66, stroke:#000, stroke-width:2px, color:#000, width:160px
    style S fill:#3B2F5E66, stroke:#000, stroke-width:2px, color:#000, width:140px
    style Clus fill:#40498E66, stroke:#000, stroke-width:2px, color:#000
    style DR fill:#366A9F66, stroke:#000, stroke-width:2px, color:#000
    style Reg fill:#348AA666, stroke:#000, stroke-width:2px, color:#000
    style Class fill:#38AAAC66, stroke:#000, stroke-width:2px, color:#000

flowchart LR
    classDef largeText font-size:18px, padding:15px;

    U(Aprendizaje\nNo Supervisado) -->|Se desea generar 'Y'| Clus(Agrupamiento)
    U --> |Se desea\nexplorar 'X'|DR(Reducción de\nDimensionalidad)

    class U,Clus,DR largeText;

    style U fill:#28192F66, stroke:#000, width:160px, stroke-width:2px, color:#000, text-align:center;
    style Clus fill:#40498E66, stroke:#000, width:160px, stroke-width:2px, color:#000, text-align:center;
    style DR fill:#366A9F66, stroke:#000, width:160px, stroke-width:2px, color:#000, text-align:center;

flowchart LR
    classDef largeText font-size:18px, padding:15px;

    U(Aprendizaje\nNo Supervisado) -->|Se desea generar 'Y'| Clus(Agrupamiento)
    U --> |Se desea\nexplorar 'X'|DR(Reducción de\nDimensionalidad)

    Clus --> Kmeans(K-means)
    Clus --> DB(HDBSCAN)
    Clus --> Hier(Jerárquico)

    DR --> PCA(PCA)
    DR --> TSNE(t-SNE)
    DR --> UMAP(UMAP)

    class U,Clus,DR largeText;

    style U fill:#28192F66, stroke:#000, width:160px, stroke-width:2px, color:#000, text-align:center;
    style Clus fill:#40498E66, stroke:#000, width:160px, stroke-width:2px, color:#000, text-align:center;
    style DR fill:#366A9F66, stroke:#000, width:160px, stroke-width:2px, color:#000, text-align:center;

    style Kmeans fill:#40498E66, stroke:#000, stroke-width:1px, color:#000, text-align:center;
    style DB fill:#40498E66, stroke:#000, stroke-width:1px, color:#000, text-align:center;
    style Hier fill:#40498E66, stroke:#000, stroke-width:1px, color:#000, text-align:center;

    style PCA fill:#366A9F66, stroke:#000, stroke-width:1px, color:#000, text-align:center;
    style TSNE fill:#366A9F66, stroke:#000, stroke-width:1px, color:#000, text-align:center;
    style UMAP fill:#366A9F66, stroke:#000, stroke-width:1px, color:#000, text-align:center;

flowchart LR
    classDef largeText font-size:18px, padding:15px;

    S(Aprendizaje\nSupervisado) -->|Y es continua| Reg(Regresión)
    S -->|Y es categórica| Class(Clasificación)

    class S,Reg,Class largeText;

    style S fill:#3B2F5E66, stroke:#000, width:140px, stroke-width:2px, color:#000, text-align:center;
    style Reg fill:#348AA666, stroke:#000, width:130px, stroke-width:2px, color:#000, text-align:center;
    style Class fill:#38AAAC66, stroke:#000, width:150px, stroke-width:2px, color:#000, text-align:center;

flowchart LR
    classDef largeText font-size:18px, padding:15px;

    S(Aprendizaje\nSupervisado) -->|Y es continua| Reg(Regresión)
    S -->|Y es categórica| Class(Clasificación)

    Reg --> Regr(Regresión)
    Reg --> RFR(Regresión\nRandom Forest)
    Reg --> NNR(Regresión\nde Red Neuronal)

    Class --> DT(Árboles de Decisión)
    Class --> RL(Regresión Logística)
    Class --> KNN(K vecinos más cercanos)
    Class --> RF(Random Forest)
    Class --> NN(Redes Neuronales)

    class S,Reg,Class largeText;

    style S fill:#3B2F5E66, stroke:#000, width:130px, stroke-width:2px, color:#000, text-align:center;
    style Reg fill:#348AA666, stroke:#000, width:130px, stroke-width:2px, color:#000, text-align:center;
    style Class fill:#38AAAC66, stroke:#000, width:130px, stroke-width:2px, color:#000, text-align:center;

    style Regr fill:#348AA666, stroke:#000, stroke-width:1px, color:#000, text-align:center;
    style RFR fill:#348AA666, stroke:#000, stroke-width:1px, color:#000, text-align:center;
    style NNR fill:#348AA666, stroke:#000, stroke-width:1px, color:#000, text-align:center;

    style DT fill:#38AAAC66, stroke:#000, stroke-width:1px, color:#000, text-align:center;
    style RL fill:#38AAAC66, stroke:#000, stroke-width:1px, color:#000, text-align:center;
    style KNN fill:#38AAAC66, stroke:#000, stroke-width:1px, color:#000, text-align:center;
    style RF fill:#38AAAC66, stroke:#000, stroke-width:1px, color:#000, text-align:center;
    style NN fill:#38AAAC66, stroke:#000, stroke-width:1px, color:#000, text-align:center;

flowchart LR
    classDef largeText font-size:18px, padding:15px; 
    
    ML("Aprendizaje Estadístico (Y = f(X))") -->|'Y' no existe| U(Aprendizaje\nNo Supervisado)
    ML -->|'Y' existe| S(Aprendizaje\nSupervisado)
    U -->|Se desea\ngenerar 'Y'| Clus(Agrupamiento)
    U --> |Se desea\nexplorar 'X'| DR(Reducción de\nDimensionalidad)
    S -->|'Y' es continua| Reg(Regresión)
    S --> |'Y' es categórica| Class(Clasificación)
    Clus --> Kmeans(K-means)
    Clus --> DB(HDBSCAN)
    Clus --> Hier(Jerárquico)
    DR --> PCA(PCA)
    DR --> TSNE(t-SNE)
    DR --> UMAP(UMAP)
    Reg --> Regr(Regresión)
    Reg --> RFR(Regresión\nRandom Forest)
    Reg --> NNR(Regresión de\nRed Neuronal)
    Class --> DT(Árboles de\nDecisión)
    Class --> RL(Regresión Logística)
    Class --> KNN(K vecinos\nmás cercanos)
    Class --> RF(Random Forest)
    Class --> NN(Redes\nNeuronales)

    class ML,U,S largeText;

    style ML fill:#FFFFFF, stroke:#000, stroke-width:2px, color:#000, width:320px
    style U fill:#28192F66, stroke:#000, stroke-width:2px, color:#000, width:160px
    style S fill:#3B2F5E66, stroke:#000, stroke-width:2px, color:#000, width:130px
    style Clus fill:#40498E66, stroke:#000, stroke-width:2px, color:#000
    style Kmeans fill:#40498E66, stroke:#000, stroke-width:1px, color:#000
    style DB fill:#40498E66, stroke:#000, stroke-width:1px, color:#000
    style Hier fill:#40498E66, stroke:#000, stroke-width:1px, color:#000

    style DR fill:#366A9F66, stroke:#000, stroke-width:2px, color:#000
    style PCA fill:#366A9F66, stroke:#000, stroke-width:1px, color:#000
    style TSNE fill:#366A9F66, stroke:#000, stroke-width:1px, color:#000
    style UMAP fill:#366A9F66, stroke:#000, stroke-width:1px, color:#000

    style Reg fill:#348AA666, stroke:#000, stroke-width:2px, color:#000
    style Regr fill:#348AA666, stroke:#000, stroke-width:1px, color:#000
    style RFR fill:#348AA666, stroke:#000, stroke-width:1px, color:#000
    style NNR fill:#348AA666, stroke:#000, stroke-width:1px, color:#000

    style Class fill:#38AAAC66, stroke:#000, stroke-width:2px, color:#000
    style DT fill:#38AAAC66, stroke:#000, stroke-width:1px, color:#000
    style RL fill:#38AAAC66, stroke:#000, stroke-width:1px, color:#000
    style KNN fill:#38AAAC66, stroke:#000, stroke-width:1px, color:#000
    style RF fill:#38AAAC66, stroke:#000, stroke-width:1px, color:#000
    style NN fill:#38AAAC66, stroke:#000, stroke-width:1px, color:#000